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CATEGORIES:Lectures & Workshops
DESCRIPTION:Geometric methods for image-based statistical analysis of brain
tumors.\n\nBiomedical studies are a common source of rich and complex imag
ing data. The statistical analysis of such datasets requires novel methodol
ogical developments due to two main challenges: (1) the functional nature o
f the data objects under study\, and (2) the nonlinearity of their represen
tation spaces. In this work\, we consider the task of quantifying and analy
zing two different types of tumor heterogeneity. The first type\, which is
represented by a probability density function\, summarizes the tumor’s text
ure information. We use the nonparametric Fisher-Rao Riemannian framework t
o develop intrinsic statistical methods on the space of probability density
functions for summarization and inference. The second type\, which is repr
esented by a parameterized\, planar closed curve\, captures the tumor’s sha
pe information. A key component of analyzing tumor shapes is a suitable met
ric that enables efficient comparisons\, provides tools for computing descr
iptive statistics and implementing principal component analysis on the tumo
r shape space\, and allows for a rich class of continuous deformations of t
umor shape. We demonstrate the utility of our framework on a dataset of Mag
netic Resonance Images of patients diagnosed with Glioblastoma Multiforme\,
a malignant brain tumor with poor prognosis.
DTEND:20230331T210000Z
DTSTAMP:20230922T211919Z
DTSTART:20230331T200000Z
GEO:32.988266;-96.750129
LOCATION:Science Learning Center (SLC)\, 1.102
SEQUENCE:0
SUMMARY:Mathematical Sciences Colloquium and Statistics Seminar by Sebastia
n Kurtek (OSU)
UID:tag:localist.com\,2008:EventInstance_42574153019482
URL:https://calendar.utdallas.edu/event/mathematical_sciences_colloquium_an
d_statistics_seminar_by_sebastian_kurtek_osu
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